where \(h_t^d\)  is the hidden state at the \(t^{th}\)  time step of the decoding LSTM.
We further improve our encoder-decoder model using an attention based sequence-to-sequence model [1]}. The attention weights act as an alignment mechanism by re-weighting the encoder hidden states that are more relevant for decoder time step.
